What is Databricks Data Intelligence Platform?
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals.
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Real user data aggregated to summarize the product performance and customer experience.
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Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.
88 Likeliness to Recommend
100 Plan to Renew
79 Satisfaction of Cost Relative to Value
Emotional Footprint Overview
Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.
+95 Net Emotional Footprint
The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.
How much do users love Databricks Data Intelligence Platform?
Pros
- Continually Improving Product
- Fair
- Friendly Negotiation
- Helps Innovate
How to read the Emotional Footprint
The Net Emotional Footprint measures high-level user sentiment towards particular product offerings. It aggregates emotional response ratings for various dimensions of the vendor-client relationship and product effectiveness, creating a powerful indicator of overall user feeling toward the vendor and product.
While purchasing decisions shouldn't be based on emotion, it's valuable to know what kind of emotional response the vendor you're considering elicits from their users.
Footprint
Negative
Neutral
Positive
Feature Ratings
Performance and Scalability
Pre-Packaged AI/ML Services
Model Training
Feature Engineering
Data Ingestion
Algorithm Diversity
Data Pre-Processing
Algorithm Recommendation
Ensembling
Openness and Flexibility
Model Tuning
Vendor Capability Ratings
Ease of Data Integration
Breadth of Features
Availability and Quality of Training
Product Strategy and Rate of Improvement
Ease of Implementation
Usability and Intuitiveness
Quality of Features
Business Value Created
Ease of IT Administration
Ease of Customization
Vendor Support
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Databricks Data Intelligence Platform Reviews
Anjali R.
- Role: Consultant
- Industry: Technology
- Involvement: IT Development, Integration, and Administration
Submitted Sep 2025
I personally liked the software
Likeliness to Recommend
What differentiates Databricks Data Intelligence Platform from other similar products?
It's Lakehouse Architecture that combines data warehousing and data lake capabilities a comprehensive MLOps platform built on MLflow for the full machine learning lifecycle, built-in generative AI features, and a collaborative, multi-language notebook environment for data scientists. This integrated, scalable platform streamlines data and AI workflows, enhances governance, and
What is your favorite aspect of this product?
It's fast ,efficient providing real time solutions to our problems
What do you dislike most about this product?
high costs, especially at scale, and its steep learning curve, particularly for those new to distributed computing or Spark
What recommendations would you give to someone considering this product?
Yes definitely
Pros
- Helps Innovate
- Continually Improving Product
- Reliable
- Enables Productivity
- Role: Consultant
- Industry: Technology
- Involvement: Business Leader or Manager
Submitted Jan 2025
Best product for unified data platform
Likeliness to Recommend
Pros
- Helps Innovate
- Continually Improving Product
- Reliable
- Performance Enhancing
Nicky R.
- Role: Information Technology
- Industry: Retail
- Involvement: End User of Application
Submitted Dec 2024
Leading data intelligence platform, for me
Likeliness to Recommend
What differentiates Databricks Data Intelligence Platform from other similar products?
Obviously, this should be its Apache spark processing engine. It is the major reason why we have been able to utilize Databricks to smoothly manage and process all data formats and types on a large scale. Its support for multiple data frameworks and languages is another area of differential, I think.
What is your favorite aspect of this product?
Its ability to unify a variety of workflows . This makes it easier for the different type of data professionals in my organization to collaborate and work together. Thereby increasing the speed and efficiency with which we can generate insights from large scale data pipelines.
What do you dislike most about this product?
There is a high chance for small sized teams to find Databricks expensive. Databricks pricing model is not favourable to small sized teams.
What recommendations would you give to someone considering this product?
For large scale data management, I think Databricks is a platform to try..
Pros
- Reliable
- Performance Enhancing
- Enables Productivity
- Unique Features
